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Journal of Bioinformatics and Computational Biology

Sabyasachi Patra, Anjali Mohapatra
Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc...
September 19, 2018: Journal of Bioinformatics and Computational Biology
Sovan Saha, Abhimanyu Prasad, Piyali Chatterjee, Subhadip Basu, Mita Nasipuri
Protein Function Prediction from Protein-Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lead to the identification of those significant proteins which are responsible for the generation of various diseases whose drugs are still yet to be discovered. So, the prediction of GO functional terms from PPIN and sequence is an important field of study. In this work, we have proposed a methodology, Multi Label Protein Function Prediction (ML_PFP) which is based on Neighborhood analysis empowered with physico-chemical features of constituent amino acids to predict the functional group of unannotated protein...
September 19, 2018: Journal of Bioinformatics and Computational Biology
Yuanfang Ren, Ahmet Ay, Travis A Gerke, Tamer Kahveci
Associations between expressions of genes play a key role in deciphering their functions. Correlation score between pairs of genes is often utilized to associate two genes. However, the relationship between genes is often more complex; multiple genes might collaborate to control the transcription of a gene. In this paper, we introduce the problem of searching pairs of genes, which collectively correlate with another gene. This problem is computationally much harder than the classical problem of identifying pairwise gene associations...
October 2018: Journal of Bioinformatics and Computational Biology
Hisham Al-Mubaid
Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional...
October 2018: Journal of Bioinformatics and Computational Biology
Yanbu Guo, Bingyi Wang, Weihua Li, Bei Yang
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectional recurrent neural network (2C-BRNN), for improving the accuracy of 8-class secondary structure prediction...
October 2018: Journal of Bioinformatics and Computational Biology
Richard Olney, Aaron Tuor, Filip Jagodzinski, Brian Hutchinson
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies are time and cost prohibitive. Computational approaches for informing experimentalists where to allocate wet-lab resources are available, including a variety of machine learning models. Assessing the accuracy of machine learning models for predicting the effects of mutations is dependent on experiments for amino acid substitutions performed in vitro...
October 2018: Journal of Bioinformatics and Computational Biology
Ronald J Nowling, Scott J Emrich
Association tests performed with the Likelihood-Ratio Test (LR Test) can be an alternative to [Formula: see text], which is often used in population genetics to find variants of interest. Because the LR Test has several properties that could make it preferable to [Formula: see text], we propose a novel approach for modeling unknown genotypes in highly-similar species. To show the effectiveness of this LR Test approach, we apply it to single-nucleotide polymorphisms (SNPs) associated with the recent speciation of the malaria vectors Anopheles gambiae and Anopheles coluzzii and compare to [Formula: see text]...
October 2018: Journal of Bioinformatics and Computational Biology
Dawid Dąbkowski, Paweł Tabaszewski, Paweł Górecki
Metagenomic studies identify the species present in an environmental sample usually by using procedures that match molecular sequences, e.g. genes, with the species taxonomy. Here, we first formulate the problem of gene-species matching in the parsimony framework using binary phylogenetic gene and species trees under the deep coalescence cost and the assumption that each gene is paired uniquely with one species. In particular, we solve the problem in the cases when one of the trees is a caterpillar. Next, we propose a dynamic programming algorithm, which solves the problem exactly, however, its time and space complexity is exponential...
October 2018: Journal of Bioinformatics and Computational Biology
Hisham Al-Mubaid, Qin Ding, Oliver Eulenstein
No abstract text is available yet for this article.
October 2018: Journal of Bioinformatics and Computational Biology
Keerthi S Shetty, Annappa B
Many biochemical events involve multistep reactions. One of the most important biological processes that involve multistep reaction is the transcriptional process. Models for multistep reaction necessarily need multiple states and it is a challenge to compute model parameters that best agree with experimental data. Therefore, the aim of this work is to design a multistep promoter model which accurately characterizes transcriptional bursting and is consistent with observed data. To address this issue, we develop a model for promoters with several OFF states and a single ON state using Erlang distribution...
October 2018: Journal of Bioinformatics and Computational Biology
Ioannis A Tamposis, Margarita C Theodoropoulou, Konstantinos D Tsirigos, Pantelis G Bagos
Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both...
August 3, 2018: Journal of Bioinformatics and Computational Biology
Zafer Aydin, Oğuz Kaynar, Yasin Görmez
Secondary structure and solvent accessibility prediction provide valuable information for estimating the three dimensional structure of a protein. As new feature extraction methods are developed the dimensionality of the input feature space increases steadily. Reducing the number of dimensions provides several advantages such as faster model training, faster prediction and noise elimination. In this work, several dimensionality reduction techniques have been employed including various feature selection methods, autoencoders and PCA for protein secondary structure and solvent accessibility prediction...
August 3, 2018: Journal of Bioinformatics and Computational Biology
Sanjeev Kumar, Suneeta Agarwal, Ranvijay
Genomic data nowadays is playing a vital role in number of fields such as personalized medicine, forensic, drug discovery, sequence alignment and agriculture, etc. With the advancements and reduction in the cost of next-generation sequencing (NGS) technology, these data are growing exponentially. NGS data are being generated more rapidly than they could be significantly analyzed. Thus, there is much scope for developing novel data compression algorithms to facilitate data analysis along with data transfer and storage directly...
June 28, 2018: Journal of Bioinformatics and Computational Biology
Aman Sharma, Rinkle Rani
Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism...
June 28, 2018: Journal of Bioinformatics and Computational Biology
Amr Alhossary, Yaw Awuni, Chee Keong Kwoh, Yuguang Mu
Dengue fever is a febrile illness caused by Dengue Virus, which belongs to the Flaviviridae family. Among its proteome, the nonstructural protein 5 (NS5) is the biggest and most conserved. It has a primer-independent RNA-dependent RNA polymerase (RdRp) domain at its C-Terminus. Zou et al. studied the biological relevance of the two conserved cavities (named A and B) within the NS5 proteins of dengue virus (DENV) and West Nile Virus (WNV) using mutagenesis and revertant analysis and found four mutations located at cavity B having effects on viral replication...
June 2018: Journal of Bioinformatics and Computational Biology
Naoki Arai, Shunsuke Yoshikawa, Nobuaki Yasuo, Ryunosuke Yoshino, Masakazu Sekijima
During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds...
June 2018: Journal of Bioinformatics and Computational Biology
Lee Sael
No abstract text is available yet for this article.
June 2018: Journal of Bioinformatics and Computational Biology
Amir Zeb, Chanin Park, Minky Son, Shailima Rampogu, Syed Ibrar Alam, Seok Ju Park, Keun Woo Lee
Proteins deacetylation by Histone deacetylase 6 (HDAC6) has been shown in various human chronic diseases like neurodegenerative diseases and cancer, and hence is an important therapeutic target. Since, the existing inhibitors have hydroxamate group, and are not HDAC6-selective, therefore, this study has designed to investigate non-hydroxamate HDAC6 inhibitors. Ligand-based pharmacophore was generated from 26 training set compounds of HDAC6 inhibitors. The statistical parameters of pharmacophore (Hypo1) included lowest total cost of 115...
June 2018: Journal of Bioinformatics and Computational Biology
Turki Turki, Zhi Wei, Jason T L Wang
Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task...
June 2018: Journal of Bioinformatics and Computational Biology
Maria Satti, Yasuhiro Tanizawa, Akihito Endo, Masanori Arita
The commensal genus Bifidobacterium has probiotic properties. We prepared a public library of the gene functions of the genus Bifidobacterium for its online annotation. Orthologous gene cluster analysis showed that the pan genomes of Bifidobacterium and Lactobacillus exhibit striking similarities when mapped to the Clusters of Orthologous Group (COG) database of proteins. When the core genes in each genus were selected based on our statistical definition of "core genome", core genes were present in at least 92% of 52 Bifidobacterium and in 97% of 178 Lactobacillus genomes...
June 2018: Journal of Bioinformatics and Computational Biology
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